scholarly journals A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

Smart Cities ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 541-555
Author(s):  
Ernesto Cipriani ◽  
Gaetano Fusco ◽  
Sergio Maria Patella ◽  
Marco Petrelli
Keyword(s):  
Particle Swarm Optimization ◽  
Network Design ◽  
Urban Areas ◽  
Design Problem ◽  
Particle Swarm ◽  
Network Design Problem ◽  
Swarm Optimization ◽  
Transit Network ◽  
Efficiency And Effectiveness ◽  
Transit Network Design

The research presented in this paper proposes a Particle Swarm Optimization (PSO) approach for solving the transit network design problem in large urban areas. The solving procedure is divided in two main phases: in the first step, a heuristic route generation algorithm provides a preliminary set of feasible and comparable routes, according to three different design criteria; in the second step, the optimal network configuration is found by applying a PSO-based procedure. This study presents a comparison between the results of the PSO approach and the results of a procedure based on Genetic Algorithms (GAs). Both methods were tested on a real-size network in Rome, in order to compare their efficiency and effectiveness in optimal transit network calculation. The results show that the PSO approach promises more efficiency and effectiveness than GAs in producing optimal solutions.

scholarly journals Hybrid Differential Evolution-Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Ahmed Tarajo Buba ◽  
Lai Soon Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Network Design ◽  
Design Problem ◽  
Hybrid Algorithm ◽  
Network Design Problem ◽  
Urban Transit ◽  
Swarm Optimization ◽  
Transit Network ◽  
Transit Network Design

This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.

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